SCHEMA - An Algorithm for Automated Product Taxonomy Mapping in E-commerce

Aanen, Steven S.; Nederstigt, Lennart J.; Vandic, Damir; Frăsincar, Flavius

doi:10.1007/978-3-642-30284-8_27

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…For this reason, in the following, we will use the terms ontology and taxonomy with the same meaning.”)the paper contains an evaluation of their alignment process by comparing to other “ontology” matchers. For instance, we exclude Aanen et al .’s (2012) paper: though the authors used the term “taxonomy mapping” throughout their paper, they compared their matcher SCHEMA with other ontology matchers by Noy and Musen (2003) and Park and Kim (2007).the paper conducts an evaluation of their alignment process by using benchmark “ontology” alignment datasets. For example, we exclude the study by Lin et al .…”

Section: Methodsmentioning

confidence: 99%

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A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

Cheng

Xia

2023

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PurposeThe purpose of this study is to provide a systematic literature review on taxonomy alignment methods in information science to explore the common research pipeline and characteristics.Design/methodology/approachThe authors implement a five-step systematic literature review process relating to taxonomy alignment. They take on a knowledge organization system (KOS) perspective, and specifically examining the level of KOS on “taxonomies.”FindingsThey synthesize the matching dimensions of 28 taxonomy alignment studies in terms of the taxonomy input, approach and output. In the input dimension, they develop three characteristics: tree shapes, variable names and symmetry; for approach: methodology, unit of matching, comparison type and relation type; for output: the number of merged solutions and whether original taxonomies are preserved in the solutions.Research limitations/implicationsThe main research implications of this study are threefold: (1) to enhance the understanding of the characteristics of a taxonomy alignment work; (2) to provide a novel categorization of taxonomy alignment approaches into natural language processing approach, logic-based approach and heuristic-based approach; (3) to provide a methodological guideline on the must-include characteristics for future taxonomy alignment research.Originality/valueThere is no existing comprehensive review on the alignment of “taxonomies”. Further, no other mapping survey research has discussed the comparison from a KOS perspective. Using a KOS lens is critical in understanding the broader picture of what other similar systems of organizations are, and enables us to define taxonomies more precisely.

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Section: Methodsmentioning

confidence: 99%

“…Spanning across different fields, taxonomy alignment (mapping, matching, merging) work flourishes in biodiversity (Franz et al. , 2016), biology (Kanehisa et al ., 2022), e-commerce (Aanen et al ., 2012), information systems and computer science (Raunich and Rahm, 2014) and many others.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

Cheng

Xia

2023

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“…At this point it is necessary to define the taxonomy model and to locate the concepts without ancestors, in order to begin to visit all the concepts. This is particular relevant in forest models 1 . Note that the ArrayList is necessary to store the visited concepts.…”

Section: Calling To the Algorithmmentioning

confidence: 99%

“…The problem of aligning taxonomies have received much attention by the research community since various knowledge based applications, including clustering algorithms, browsing support interfaces, and recommendation systems, perform more effectively when they are supported with domain describing taxonomies, which help to resolve ambiguities and provide context [3]. Furthermore, this problem is of great interest on a number of application areas, especially in scientific [6], business [1] [14], and web data integration [4] [15].…”

Section: Related Workmentioning

confidence: 99%

SIFT: An Algorithm for Extracting Structural Information From Taxonomies

Martinez‐Gil¹

2016

Preprint

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In this work we present SIFT, a 3-step algorithm for the analysis of the structural information represented by means of a taxonomy. The major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step. This method is particular relevant in scenarios where taxonomy alignment techniques exploiting textual information from taxonomy nodes cannot operate successfully.

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“…We do not focus on the required preprocessing steps, such as HTML table extraction [4] and product duplicate detection [1,6]. Instead, the presence of product information on the collected Web pages in the form of key-value pairs is assumed.…”

Section: Frameworkmentioning

confidence: 99%

Ontology Population from Web Product Information

Vandic

Nederstigt

Aanen

2014

Lecture Notes in Computer Science

Self Cite

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With the vast amount of information available on the Web, there is an increasing need to structure Web data in order to make it accessible to both users and machines. E-commerce is one of the areas in which growing data congestion on the Web has serious consequences. This paper proposes a framework that is capable of populating a product ontology using tabular product information from Web shops. By formalizing product information in this way, better product comparison or recommendation applications could be built. Our approach employs both lexical and syntactic matching for mapping properties and instantiating values. The performed evaluation shows that instantiating consumer electronics from Best Buy and Newegg.com results in an F1 score of approximately 77%.

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SCHEMA - An Algorithm for Automated Product Taxonomy Mapping in E-commerce

Cited by 7 publications

References 21 publications

A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

A systematic review of methods for aligning, mapping, merging taxonomies in information sciences

SIFT: An Algorithm for Extracting Structural Information From Taxonomies

Ontology Population from Web Product Information

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